摘要
针对电厂开关检测方法无法应对现实开集环境,对稀有类别识别准确率低的现状,将目标识别问题转化为相似性度量问题,并提出新算法。新算法基于深度度量学习的三元组网络,利用加入SE Block的ResNet-18提取特征,并利用跨批次挖掘增强学习效果。为评估算法性能,创建了一个包含3300张开关图片的数据集,并使用新算法在该数据集上进行了闭集测试、开集测试、小样本测试。结果表明:新算法在闭集状态下具有良好的区分能力,不仅能准确识别训练集中的类别,还能有效区分训练时未遇到的及出现频率较低的状态。由此表明,该算法不仅适用于现实世界的开集环境,而且能显著提升对小样本数据的识别精度。
In response to the current power plant switch detection methods that are unable to cope with realworld open-set environments and the low accuracy in recognizing rare categories,the target recognition problem is transformed into a similarity measurement issue,and a new algorithm is proposed.The new algorithm is based on the triplet network of deep metric learning,using a ResNet-18 with an added SE Block to extract features,and enhances learning effects by cross-batch mining.To evaluate the performance of the algorithm,a dataset with 3300 switch images was created.The algorithm was tested on the self-built dataset for closedset testing,open-set testing,and few-shot testing.The experimental results show that the algorithm demonstrates excellent discrimination ability in the closed-set state.It can not only accurately identify the categories in the training set but also effectively distinguish states that were not encountered during training and those with lower occurrence frequencies.This capability indicates that the algorithm is not only suitable for real-world open-set environments but also significantly improves the recognition accuracy for small-sample data.
作者
冯天任
陈世峰
FENG Tianren;CHEN Shifeng(China Institute of Atomic Energy,Beijing 102413,China;Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China)
出处
《集成技术》
2024年第5期30-39,共10页
Journal of Integration Technology
基金
深圳市科技创新委员会基础研究重点项目(JCYJ20200109114835623)
深圳市科技创新委员会技术攻关重点项目(JSGG20220831105002004)。
关键词
深度度量学习
三元组网络
注意力机制
开关状态识别
deep metric learning
triplet network
attention mechanism
switch state recognition
